(1) Field of the Invention
The present invention relates to sonar image feature extraction and more specifically to fitting a parameterized statistical model to sonar image data to extract features that describe image textures for follow-on pattern classification tasks.
(2) Description of the Prior Art
As is known in the art, parameterized statistical modeling of sonar imagery can produce simple, robust quantitative descriptors for complex textured seabed environments. One such known model is a correlated K-distribution model for synthetic aperture radar (SAR) image pixels, with parameters defining spatial correlation lengths in vertical and horizontal dimensions along with a spatial frequency component in one direction to model periodic variation in the autocorrelation function (ACF).
The K-distribution has been shown to adequately describe the single-point amplitude and intensity pixel statistics of sonar images. One known method to derive the K-distribution probability density function (pdf) equation is to treat the sonar intensity scene as being composed of exponentially-distributed speckle modulated by a gamma-distributed parameter in the form of a product of two random variables. Along with its intuitive appeal, this convention allows for the introduction of correlated texture through a correlated gamma distribution.
As is known to those of skill in the art, the analytic expression for the pdf for a K-distributed sonar image intensity pixel I(x,y) is:
                                                                        p                I                            ⁡                              (                x                )                                      =                                          α                                  Γ                  ⁡                                      (                    v                    )                                                              ⁢                                                (                                      α                    2                                    )                                v                            ⁢                                                (                  x                  )                                                                      v                    -                    1                                    2                                            ⁢                                                K                                      v                    -                    1                                                  ⁡                                  (                                      α                    ⁢                                          x                                                        )                                            ⁢                              u                ⁡                                  (                  x                  )                                                              ;                      v            >            0                          ,                            (        1        )            where α is the scale parameter, ν is the shape parameter of the distribution and Kν-1 is the modified Bessel function of the second kind.
The pixel statistical parameter estimation described above relies on independence between pixel samples. Thus, the correlation between neighboring pixels is ignored. As such, the pdf in Equation (1) describes the one-dimensional statistics of the image pixel values, but does not describe texture.
What is needed is a parameterized statistical model that can be fitted to synthetic aperture sonar image data to extract features or descriptors for follow-on pattern classification tasks. What is further needed is for the model to extract parameters that describe image textures such as correlation length, periodicity or ripple, orientation, and other statistical descriptors for use in automatic target recognition, through-the-sensor environmental characterization, and texture segmentation schemes.